Mastering the Data Lifecycle for Governed AI-BI in the Cloud: From Ingestion to Auditable Deletion (Published)
The rapid evolution of AI-powered Business Intelligence (BI) solutions demands robust data governance frameworks that span the entire data lifecycle in cloud environments. Organizations face intensifying regulatory pressures, particularly from GDPR requirements concerning data erasure and storage limitations. The successful implementation of data governance requires integrated solutions addressing ownership, classification, ingestion, storage, and retention management. Through cloud-native tools and automated processes, enterprises can achieve both regulatory compliance and operational efficiency. The adoption of sophisticated data lifecycle management strategies, leveraging advanced capabilities from major cloud providers, enables organizations to maintain control over their data assets while supporting innovative AI-BI implementations. The integration of automated classification systems, intelligent storage management, and comprehensive audit mechanisms provides organizations with the necessary foundation to address evolving regulatory requirements while maximizing the value of their data assets. These frameworks enable seamless adaptation to changing compliance landscapes, ensuring sustainable growth and innovation in AI-powered business intelligence solutions.
Keywords: AI-driven analytics, cloud governance, data lifecycle management, enterprise data protection, regulatory technology
Next-Generation Supply Chains: Achieving ‘One Delivery’-A Single, Seamless Flow from Factory to Front Door with AI, IoT, and Autonomous Technologies (Published)
This article examines the transformative impact of artificial intelligence, Internet of Things, and autonomous technologies on modern supply chain systems. In today’s hyper-connected digital economy, consumers expect not just products, but experiences—seamless, reliable, and personalized from the moment an order is placed until it arrives at their doorstep. This expectation has given rise to the concept of “One Delivery”: a unified logistics paradigm in which every link of the supply chain—from manufacturing and warehousing to transportation and last-mile fulfillment—operates as a single, uninterrupted flow. Unlike traditional models that treat each segment as a discrete step with handoffs and potential delays, One Delivery envisions a continuous journey powered by real-time data, intelligent decision-making, and autonomous execution. As consumer expectations evolve in an increasingly digital marketplace, organizations are compelled to develop integrated, agile supply chains capable of delivering products through unified journeys. The convergence of these advanced technologies enables unprecedented operational visibility, predictive capabilities, and adaptive responsiveness throughout the supply chain ecosystem. By exploring the technological pillars enabling “one delivery” systems, integration strategies, implementation challenges, and strategic considerations, a comprehensive framework emerges for achieving seamless delivery that enhances operational efficiency while meeting the demands of today’s discerning consumers. The insights presented offer both theoretical understanding and practical guidance for organizations navigating the paradigm shift toward next-generation supply chains in a hyper-connected digital economy.
Keywords: AI-driven analytics, Autonomous technologies, IoT-enabled connectivity, Next-generation supply chains, Unified delivery